Bifurcation analysis of a TaO memristor model

This paper presents a study of bifurcation in the time-averaged dynamics of TaO memristors driven by narrow pulses of alternating polarities. The analysis, based on a physics-inspired model, focuses on the stable fixed points and on how these are affected by the pulse parameters. Our main finding is the identification of a driving regime when two stable fixed points exist simultaneously. To the best of our knowledge, such bistability is identified in a single memristor for the first time. This result can be readily tested experimentally, and is expected to be useful in future memristor circuit designs

Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications

In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death

A pragmatic gaze on stochastic resonance based variability tolerant memristance

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Stochastic Resonance (SR) is a nonlinear system specific phenomenon, which was demonstrated to lead to system unexpected (counter-intuitive) performance improvements under certain noise conditions. Memristor, on the other hand, is a fundamentally nonlinear circuit element, thus susceptible to benefit from SR, which recently came in the spotlight of the emerging technologies potential candidates. However, at this time, the variability exhibited by manufactured memristor devices within the same array constitutes the main hurdle in the road towards the commercialisation of memristor-based memories and/or computing units. Thus, in this paper, memristor SR effects are explored, assuming various memristor models, and SR-based memristance range enhancement, tolerant to device-to-device variability, is demonstrated. Our experiments reveal that SR can induce significant R MAX /R MIN ratio increase under up to 60% variability, getting as high as 3.4× for 29 dBm noise power.Peer ReviewedPostprint (author's final draft

UNDERSTANDING MEMRISTORS AND SELECTORS FOR FUTURE STORAGE AND COMPUTING APPLICATIONS: MODELING AND ANALYSIS

The memristor and selector devices are the most promising candidates in the research of emerging memory technologies and neuromorphic computing applications. To understand the device properties and guide for future applications, models for those devices based on physical mechanisms are essential. We developed models for two popular memristors and a selector. We developed a SPICE-compatible compact model of TiO2-TiO2-x memristors based on classic ion transportation theory. Our model is shown to simulate important dynamic memristive properties like real-time memristance switching, which are critical in memristor-based analog circuit designs. The model, as well as its analytical approximation, is validated with the experimentally obtained data from real devices. Minor deviations of our model from the measured data are also analyzed and discussed. We illustrate a heuristic two-state-variable memristor model of charged O vacancy drift resistive switches that includes the effects of internal Joule heating on both the electronic transport and the drift velocity (i.e. switching speed) of vacancies in the switching material. The dynamical state variables correspond to the cross-sectional area of a conducting channel in the device and the gap between the end of the channel and one of the electrodes. The model was calibrated against low voltage pulse-sweep and state-test data collected from a TaOx memristor so that the contributions of the channel gap, area and temperature to switching can be analyzed. The model agrees well with experimental results for long switching times and low-to-intermediate voltage operation. A selector device that demonstrates high nonlinearity, low switching voltage and volatility was fabricated using HfOx materials with Ag electrodes. The electronic conductance of such volatile selector device was studied under both static and dynamic conditions, with DC and AC measurements respectively. From experimental observations, a compact model is developed in this study to illustrate the physical process of the formation and dissipation of Ag filament for electron transport through the device. A dynamic capacitance model is used to fit the transient current traces under different voltage bias through the device and allow the extraction of parameters associated with the various parasitic components in the device

Bio-inspired Neuromorphic Computing Using Memristor Crossbar Networks

Bio-inspired neuromorphic computing systems built with emerging devices such as memristors have become an active research field. Experimental demonstrations at the network-level have suggested memristor-based neuromorphic systems as a promising candidate to overcome the von-Neumann bottleneck in future computing applications. As a hardware system that offers co-location of memory and data processing, memristor-based networks represent an efficient computing platform with minimal data transfer and high parallelism. Furthermore, active utilization of the dynamic processes during resistive switching in memristors can help realize more faithful emulation of biological device and network behaviors, with the potential to process dynamic temporal inputs efficiently. In this thesis, I present experimental demonstrations of neuromorphic systems using fabricated memristor arrays as well as network-level simulation results. Models of resistive switching behavior in two types of memristor devices, conventional first-order and recently proposed second-order memristor devices, will be first introduced. Secondly, experimental demonstration of K-means clustering through unsupervised learning in a memristor network will be presented. The memristor based hardware systems achieved high classification accuracy (93.3%) on the standard IRIS data set, suggesting practical networks can be built with optimized memristor devices. Thirdly, implementation of a partial differential equation (PDE) solver in memristor arrays will be discussed. This work expands the capability of memristor-based computing hardware from ‘soft’ to ‘hard’ computing tasks, which require very high precision and accurate solutions. In general first-order memristors are suitable to perform tasks that are based on vector-matrix multiplications, ranging from K-means clustering to PDE solvers. On the other hand, utilizing internal device dynamics in second-order memristors can allow natural emulation of biological behaviors and enable network functions such as temporal data processing. An effort to explore second-order memristor devices and their network behaviors will be discussed. Finally, we propose ideas to build large-size passive memristor crossbar arrays, including fabrication approaches, guidelines of device structure, and analysis of the parasitic effects in larger arrays.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147610/1/yjjeong_1.pd

Bifurcation and Chaos in Fractional-Order Systems

This book presents a collection of seven technical papers on fractional-order complex systems, especially chaotic systems with hidden attractors and symmetries, in the research front of the field, which will be beneficial for scientific researchers, graduate students, and technical professionals to study and apply. It is also suitable for teaching lectures and for seminars to use as a reference on related topics